Fast, accurate, and reproducible image segmentation is vital to the diagnosis, treatment, and evaluation of many medical situations. We present development and application of a semi-supervised method for segmenting normal and abnormal brain tissues from magnetic resonance images (MRI) of stroke patients. The method does not require manual drawing of the tissue boundaries. It is therefore faster and more reproducible than conventional methods. The steps of the new method are as follows: (1) T2- and T1-weighted MR images are co-registered using a head and hat approach. (2) Intracranial brain volume is segmented from the skull, scalp, and background using a multi-resolution edge tracking algorithm. (3) Additive noise is suppressed (image is restored) using a non-linear edge-preserving filter which preserves partial volume information on average. (4) Image nonuniformities are corrected using a modified lowpass filtering approach. (5) The resulting images are segmented using a self organizing data analysis technique which is similar in principle to the K-means clustering but includes a set of additional heuristic merging and splitting procedures to generate a meaningful segmentation. (6) Segmented regions are labeled white matter, gray matter, CSF, partial volumes of normal tissues, zones of stroke, or partial volumes between stroke and normal tissues. (7) Previous steps are repeated for each slice of the brain and the volume of each tissue type is estimated from the results. Details and significance of each step are explained. Experimental results using a simulation, a phantom, and selected clinical cases are presented.